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Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models

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Publicado no:Remote Sensing vol. 17, no. 12 (2025), p. 2001-2022
Autor principal: Yang, Weiguang
Outros Autores: Fu Huaiyuan, Xu Weicheng, Wu Jinhao, Liu, Shiyuan, Li, Xi, Tan Jiangtao, Lan Yubin, Zhang, Lei
Publicado em:
MDPI AG
Assuntos:
United Kingdom > UK
Shenzhen China
China
Reflectance
Software
Models
Sensors
Calibration
Cameras
Remote sensing
Agriculture
Decision trees
Data conversion
Accuracy
Vegetation
Wind
Vegetation index
Precision agriculture
Radiometric correction
Cost analysis
Multisensor applications
Climate
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Resumo:Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R2 values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms.
ISSN:2072-4292
DOI:10.3390/rs17122001
Fonte:Advanced Technologies & Aerospace Database